{
 "metadata": {
  "name": "analysis"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas.io.data import DataReader\n",
      "from pandas.io.data import DataFrame\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.ticker as ticker\n",
      "from datetime import datetime\n",
      "from datetime import timedelta\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import urllib\n",
      "import codecs\n",
      "import csv \n",
      "import os\n",
      "import glob\n",
      "import json\n",
      "import requests"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import talib"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ticker_from_csv(ticker):\n",
      "    filename = ticker + \".csv\"\n",
      "    df = DataFrame.from_csv(filename)\n",
      "    return df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ticker_weekly(df):\n",
      "    ''' return weekly '''\n",
      "    mon = df.resample('W-MON')\n",
      "    fri = df.resample('W-FRI')\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ticker_scan(df):\n",
      "    ''' return an analyzed DataFrame '''\n",
      "    columnName = ['p12','p26','v12','v26', 'pAv']\n",
      "    rs = pd.DataFrame(index=df.index, columns=columnName)\n",
      "    rs['p12'] = pd.rolling_mean(df['Adj Close'],12, min_periods=2)\n",
      "    rs['p26'] = pd.rolling_mean(df['Adj Close'],26, min_periods=2)\n",
      "    rs['v12'] = pd.rolling_mean(df['Volume'],12, min_periods=2)\n",
      "    rs['v26'] = pd.rolling_mean(df['Volume'],26, min_periods=2)\n",
      "    return rs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ticker_plot(origin, scanned):\n",
      "    ''' plot the data '''\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = ticker_from_csv('amrs')\n",
      "df.tail()\n",
      "out = ticker_scan(df)\n",
      "out = out[-26:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(out)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f95acdba710>,\n",
        " <matplotlib.lines.Line2D at 0x7f95acdba8d0>,\n",
        " <matplotlib.lines.Line2D at 0x7f95acdbaa10>,\n",
        " <matplotlib.lines.Line2D at 0x7f95acdbab90>,\n",
        " <matplotlib.lines.Line2D at 0x7f95acdbad10>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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BoUPdXY3qYMUOB0/bbPxvQQFT/fz4dUgIV/r5ubusLkO7s5RqrS1bYPp0DZAe\n7svqah6zWtl24gSLhg3j/YkTsfTv7+6yegQNEdW7JSfDAw+4uwrVAUSEd8rL+ZPVSlZFBbeaTHwV\nE8PQrjodSTel3Vmq9zp4EK6+GvLyoCMuBKTcol6EV0+c4FGrFXt9PfeEhLAkMJB+3f3EwE6g3VlK\ntcbzz0NSkgZIByqpqyOrooI6Ebw9PPDu0wdvDw8u6dPHeb9F21m/AzhEcDQ2On/WnvO7s73p/pGa\nGp6y2Rh+ySX89vLLuS4goOU1x1W70z0R1TvV1UFIiHFxnotcLlS1znGHg7fLy3nLbifTbifv9Gli\nfH3p36dPiy/+2nOCoLax8bxwEOCSs0LlQj/PDZ4ALy9+ERzM9/RguUt0T0Sp7+q114wTDDVA2qTY\n4XAGxlt2OzaHgx/4+fEjPz+SR41ioo8Pnron0KNpiKjeSSdbdElBbS1v2e281bS3UeRwMNXPjx/5\n+7MsOJjogQO1+6iX0e4s1fsUFcHo0caki+1wedCeyNHYyKHqaj6rquKzU6f4vKqKz6qqqG5o4If+\n/vzIz494f3/Ga2j0KJ16PRG73c7111/P6NGjGTNmDFlZWZSWlpKQkEBERASJiYnY7Xbn89euXYvF\nYiEyMpKMjAxne3Z2NlFRUVgsFlasWOFsr62tZcGCBVgsFuLi4jh27JjzsZSUFCIiIoiIiGDTpk2u\nvgXVW23eDD/7mQYIxjDYgtpa0ktLeTQvj8VffMGEjz/G7913mXfwINtPnsTH05Nbhg/nnehoTn7/\n+6SNG8ddISFM9PHRAFEgLlqyZIls3LhRRETq6urEbrfLvffeK+vXrxcRkXXr1snKlStFROTAgQMy\nYcIEcTgckpubK2FhYdLY2CgiIlOmTJGsrCwREbnmmmtk586dIiLyzDPPyPLly0VEJDU1VRYsWCAi\nIiUlJTJy5EgpKyuTsrIy5/2zteFtqZ6usVEkMlLk3XfdXYlbFNbWyrbjx+XunByZtm+fBLz7rgx5\n9125at8+uTMnR5ILCmRPRYVU19e7u1TlBq58d7r0bWu322XEiBHntY8aNUqKiopERKSwsFBGjRol\nIiIPP/ywrFu3zvm8GTNmyAcffCAFBQUSGRnpbH/ppZfkl7/8pfM5H374oYgYITVkyBAREXnxxRfl\nlltucS7zy1/+Ul566aWWb0pDRH2T998XiYgwwqSHq29slE8rK+VZm01+fvCgjPjgAxn0zjty7aef\nypqjR2VE7N3cAAAZUElEQVRXSYkU1tY6/6BTypXvTpcOrOfm5jJ06FBuuukmPv30UyZNmsTjjz9O\ncXExgYGBAAQGBlJcXAxAQUEBcXFxzuXNZjM2mw0vLy/MZrOz3WQyYbPZALDZbISEhADg6emJn58f\nJSUlFBQUtFimeV3nWnXW5R7j4+OJj4935a2qnqYHT7Z4qqGBrIoK3isv5/2KCj6sqGCYlxffbzrw\nff9llxHZv3+vmtpcXVxmZiaZmZltWodLIVJfX8/evXt5+umnmTJlCnfeeSfr1q1r8RyPpou+u8uq\nNlwzWPVQVVXwyitw4IC7K2kXpxoaeLOsjN1lZbxXXs6hmhqiBw7kSl9flg8fzubISJ3iQ13UuX9g\nr169utXrcClEzGYzZrOZKU0X8Ln++utZu3YtQUFBFBUVERQURGFhIcOGDQOMPQyr1epcPj8/H7PZ\njMlkIj8//7z25mXy8vIYPnw49fX1lJeXExAQgMlkapGcVquVq666ypW3oXqbV16BK6+E4GB3V+IS\nEWF/VRXppaWkl5byUWUlMT4+JA4ezFMWC5N8fLhEpzJXncylT1xQUBAhISF89dVXALz++uuMHTuW\n6667jpSUFMAYQTVnzhwAZs2aRWpqKg6Hg9zcXHJycoiJiSEoKAhfX1+ysrIQETZv3szs2bOdyzSv\na9u2bUyfPh2AxMREMjIysNvtlJWVsXv3bmbMmNG2raB6h02b4MYb3V1Fq9jr69l24gTLvvwS8wcf\nMHv/fo6ePs2dZjOFV17JG9HRrLzsMq7089MAUe7h6gGYTz75RCZPnizjx4+Xn/70p2K326WkpESm\nT58uFotFEhISWoyaWrNmjYSFhcmoUaMkPT3d2b5nzx4ZN26chIWFye233+5sP336tMybN0/Cw8Ml\nNjZWcnNznY8lJydLeHi4hIeHywsvvHBebW14W6qnOn5cxM9PpLra3ZVcVENjo3xcUSF/OHpUvr93\nr/i8/bZc++mn8qTVKl9VVelBcNWhXPnu1JMNVe/w3HPw5pvGddS7IBHhUauVP1mtDPHyYubgwcwc\nPJipfn46+6zqNDp3llLf5OWX4dZb3V3FBZ1ubOTmQ4c4VF3Ne3qxJNXN6J6I6vlOnDAmWywshH79\n3F1NC4W1tczZv58R/fqRPGoU/XWvQ7lRp057olS38Y9/wMyZXS5Asisridm7l+uGDOGl0aM1QFS3\npN1Zqufrgl1ZW48f51c5OTwXEcHP9PruqhvT7izVsx0/blwzpIt0ZTWKsProUVKKi0kbN45onQRS\ndSF6YF2pc736KlxzTZcIkKqGBpK+/JLC2lqyrriCQD2bXPUAekxE9Wwvvwzz5rm7CqynT/ODffsY\n2Lcv/4mO1gBRPYZ2Z6meq4t0ZX1QXs7cAwe4OySEe8xmt84pp9TFaHeWUmfrAl1Zm4uKuOfwYZ6P\njOTHAQFuq0OpjqIhonouN47KahDhd7m5bDtxgjejoxk7YIBb6lCqo2l3luqZ3NiV9UVVFXcdPkxt\nYyPbxo4lwMurU19fKVfpyYZKNXNDV1ZuTQ03fvklP/rkE6b5+5MxfrwGiOrxtDtL9Uxbt8KvftUp\nL1VYW8sfjx0j9fhxbjOZyImNxc9T/2up3kG7s1TP00ldWSV1dazPy2NjYSE3BQezMiRErySoujUd\nnaUUdHhXVkV9PX/Oz+cpm415Q4fy2ZQpmC65pENeS6muTkNE9Twd1JVV09DAMwUFPJqXx4zBg8m6\n4grCusCZ8Eq5k3ZnqZ6lA7qyHI2NbCws5I/HjhHn68tDI0bokF3VI2l3llLt3JX12alTzNm/n4j+\n/dkeFcVkH592Wa9SPYWGiOpZtm6F225rl1WdrKtjzv79rAoNZUlQULusU6meRruzVM/Rjl1Z9SLM\n+PRTJvv4sD4srJ0KVKpr05MNVe/2j3+0W1fWrw8fxrtPHx4eObIdClOq59IQUT3Hyy/D/PltXk1K\nURGvlZTw4ujR9NUZd5W6KO3OUj1DO3VlfVRRwY8//5y3oqMZoyOwVC/Tqd1ZDQ0NTJw4keuuuw6A\n0tJSEhISiIiIIDExEbvd7nzu2rVrsVgsREZGkpGR4WzPzs4mKioKi8XCihUrnO21tbUsWLAAi8VC\nXFwcx44dcz6WkpJCREQEERERbNq0ydXyVU/TDl1ZhbW1zD1wgP8bNUoDRKnvyOUQeeKJJxgzZozz\nAjvr1q0jISGBr776iunTp7Nu3ToADh48yJYtWzh48CDp6enceuutzqRbvnw5GzduJCcnh5ycHNLT\n0wHYuHEjAQEB5OTkcNddd7Fy5UrACKqHHnqIjz76iI8++ojVq1e3CCvVi7WxK6u2sZG5Bw7w38HB\nzB4ypB0LU6pncylE8vPzee2117j55pudgbBjxw6SkpIASEpKIi0tDYDt27ezaNEivLy8CA0NJTw8\nnKysLAoLC6msrCQmJgaAJUuWOJc5e11z587ljTfeAGDXrl0kJibi7++Pv78/CQkJzuBRvdjx45Cd\nDTNnurS4iHBbTg5B3t48cPnl7VycUj2bS+eJ3HXXXTz66KNUVFQ424qLiwkMDAQgMDCQ4uJiAAoK\nCoiLi3M+z2w2Y7PZ8PLywmw2O9tNJhM2mw0Am81GSEiIUaCnJ35+fpSUlFBQUNBimeZ1XciqVauc\n9+Pj44mPj3flraru4B//gGuvdbkr69mCAj6oqOCDiRPpowfSVS+SmZlJZmZmm9bR6hD517/+xbBh\nw5g4ceI3vriHh4fbryN9doioHu7ll10+wfBtu53VR4/y3hVX4KPTt6te5tw/sFevXt3qdbS6O+v9\n999nx44djBgxgkWLFvGf//yHxYsXExgYSFFREQCFhYUMGzYMMPYwrFarc/n8/HzMZjMmk4n8/Pzz\n2puXycvLA6C+vp7y8nICAgLOW5fVam2xZ6J6oTZ0ZeWdPs2CgwfZPHo04TqRolIuaXWIPPzww1it\nVnJzc0lNTeWqq65i8+bNzJo1i5SUFMAYQTVnzhwAZs2aRWpqKg6Hg9zcXHJycoiJiSEoKAhfX1+y\nsrIQETZv3szs2bOdyzSva9u2bUyfPh2AxMREMjIysNvtlJWVsXv3bmbMmNEuG0J1Uy52ZVU3NPDT\n/fu5JySExMGDO6g4pXq+Nu+/N3db/eY3v2H+/Pls3LiR0NBQtm7dCsCYMWOYP38+Y8aMwdPTkw0b\nNjiX2bBhAzfeeCM1NTVce+21zGz6a3LZsmUsXrwYi8VCQEAAqampAAwePJjf//73TJkyBYAHH3wQ\nf3//tr4F1Z250JUlIvz3oUOMHjCAe3RPVqk20ZMNVffl4gmGf7Jaeam4mHcnTqRf374dWKBS3YtO\nBa96Fxe6snaVlvKY1UrWFVdogCjVDnTuLNV9bd0K8+Z956d/XVPDki++YOuYMVx26aUdWJhSvYd2\nZ6nuqZVdWbWNjcTu3ct/BwfzK5OpEwpUqvvRqeBV79HKrqz7jxxh5KWXcuvw4R1cmFK9i4aI6p5a\n0ZWVUVrKyydO8NdRo9x+EqxSPY2GiOpecnJg8WL46qvvdILhCYeDm778kpTISAK8vDqhQKV6Fw0R\n1T3k5sLSpfC97xnHQg4e/NauLBFh6aFD/DwwkKsGDeqkQpXqXXSIr+rarFb44x9h2zb41a/g66/h\nO55g+mxBAYUOB6+MHdvBRSrVe+meiOqaCgqMM9Gjo2HwYKP76qGHvnOAHKiq4sGjR3lx9Gi8++jH\nXKmOov+7VNdSXAx33w3jxsEll8AXX8DatRAQ8J1XcbqxkRsOHmTdyJFE9O/fgcUqpTREVNdw8iSs\nXAmjR0N9PezfD489Bk2zQbfG/UeOYOnfn6VBQR1QqFLqbBoiyr3KyuCBB2DUKCgvh08/hSefBBfP\n50gvLeWVEyf4S0SEDudVqhNoiCj3KCuDBx8Ei8U4/rFnD/zv/0LTFS1dcdzhYOmXX7Jp9GgG63Be\npTqFhojqXHY7rFplhIfVCllZkJwMI0a0abUiwk1ffklSUBDxenkApTqNhojqHOXlsHo1hIfD0aPw\n4YdGeISFtcvqnyko4HhdHatDQ9tlfUqp70bPE1Edq7wcnnjCOM7x4x/DBx8YeyHtaH9VFauPHuX9\niRN1OK9SnUz/x6mOUVFhnCQYHm6cIPjBB5CS0u4BcrqxkUUHD/LIyJFYdDivUp1OQ0RdWEUF1NZC\na6fUr6iANWuMbqpDh+C992DTpnYPj2YrDx9mdP/+3KjDeZVyC+3OUmc4HMb0Ik8+CZ99Zpyv0dgI\n/fvDgAFnbuf+3twmAqmpkJgI775rDNvtQDtLSkg7eZJPJk/W4bxKuYmGiIKiInjuOeM2ejTcfz/8\n5CfQty/U1UF1NVRVnbl90++nT8Pbb0NkZIeXXOxwsOzQIV4aM4ZBOpxXKbfREOnNPv7Y2Ov4179g\n/nzIyDCmGzmblxf4+Rm3LqBBhM+rqrjv8GFuCgriRzqcVym30svj9jZnd1kVFxsz4y5dakxy2AXV\nNjbycWUl79jtvFNezvsVFQR5e5M4aBCPhYXhpaOxlGo3rnx3aoj0Fmd3WY0ZA7fffqbLqgupqK/n\n/YoKZ2jsPXWKyP79mernx1Q/P37g58cwb293l6lUj9Rp11i3Wq1MmzaNsWPHMm7cOJ588kkASktL\nSUhIICIigsTEROx2u3OZtWvXYrFYiIyMJCMjw9menZ1NVFQUFouFFStWONtra2tZsGABFouFuLg4\njh075nwsJSWFiIgIIiIi2LRpkytvoWerqoIjR4wT+tLSjCsBjh4NhYVGl9Xrr8Ps2V0iQI47HGw7\ncYIVOTlcsWcPwz/4gHV5efTx8OCByy+n8HvfY8+kSfw5PJyfDR2qAaJUF+PSnkhRURFFRUVER0dz\n6tQpJk2aRFpaGs8//zxDhgzhvvvuY/369ZSVlbFu3ToOHjzIDTfcwMcff4zNZuPqq68mJycHDw8P\nYmJiePrpp4mJieHaa6/ljjvuYObMmWzYsIH9+/ezYcMGtmzZwquvvkpqaiqlpaVMmTKF7OxsACZN\nmkR2djb+Z/WN97g9kcZGKCkxup+Ki+H4ceN29v2zfxeBwEBjBtxhw+BHP4Jly6ALXN2vpK6Ot+x2\n3my65dfW8oOmvYypfn5M8vHhEu2iUsotXPnudOnAelBQEEFN4/IHDhzI6NGjsdls7Nixg7feeguA\npKQk4uPjWbduHdu3b2fRokV4eXkRGhpKeHg4WVlZXH755VRWVhITEwPAkiVLSEtLY+bMmezYsYPV\nq1cDMHfuXG677TYAdu3aRWJiojM0EhISSE9PZ+HCha68FfdpaGgZDEVFZ+6f+/vJk+DrawRD8605\nIGJjz9xvbh8wALrIkFd7fT1vnxUaR2pq+L6fH9P8/Xl+1Cgm+vjg2UVqVUq1XptHZx09epR9+/YR\nGxtLcXExgYGBAAQGBlJcXAxAQUEBcXFxzmXMZjM2mw0vLy/MZrOz3WQyYbPZALDZbIQ0zejq6emJ\nn58fJSUlFBQUtFimeV3nWrVqlfN+fHw88fHxbX2r311dnTEzbX6+cbNaz9xvvhUXGyOemkMhKOjM\n/dGjW/4+dCh0k26cyvp63ikvd4bGoepqvufrS7y/P89aLEz28dGD4Up1EZmZmWRmZrZpHW0KkVOn\nTjF37lyeeOIJfHx8Wjzm4eHh1hPAzg6RdtHYaExffvLk+TebrWVAnDxpfPmbzWdul10GV1555vfg\nYGP4bDclIhQ4HOyvqmJ/VRUHqqr4rKqKQ9XVTPHxYZq/P4+HhxPj46PzWSnVRZ37B3Zz709ruBwi\ndXV1zJ07l8WLFzNnzhzA2PsoKioiKCiIwsJChjVdlc5kMmG1Wp3L5ufnYzabMZlM5Ofnn9fevExe\nXh7Dhw+nvr6e8vJyAgICMJlMLZLTarVy1VVXfbeiRYwT48rLz9wqKs7ct9uNLqYLBUVZmdGlNGRI\ny1tAAIwcCT/84ZmACAoCz55zCs6Js8Oiutp5/9I+fRjbvz/jBgzge76+3BwczBU+PlyqoaFUr+HS\nN52IsGzZMsaMGcOdd97pbJ81axYpKSmsXLmSlJQUZ7jMmjWLG264gbvvvhubzUZOTg4xMTF4eHjg\n6+tLVlYWMTExbN68mTvuuKPFuuLi4ti2bRvTp08HIDExkd/+9rfY7XZEhN27d7N+/frzi7zuuvND\noqLC6BZqPnnOz88Ihub7/v5GMIwceX5YDBrUo4LhQhpF+KqmhqyKCrIrK51hUS/CuAEDGDtgAFED\nBrBo2DDGDhjAkG68J6WUah8ujc569913+eEPf8j48eOdXVZr164lJiaG+fPnk5eXR2hoKFu3bnUe\nAH/44YdJTk7G09OTJ554ghkzZgDGEN8bb7yRmpoarr32Wudw4draWhYvXsy+ffsICAggNTWV0KZr\nRTz//PM8/PDDADzwwAMkJSW1fFMeHkhaWsuwaA4M/eJzOuFwkFVZSVZFBVkVFXxcWckgT09ifX2Z\n7OPD+KbgCPb21rmplOoF9GTDJj1uiG87ON3YyCenTvFhU2BkVVRQWl/PFB8fYn19iW36qedhKNV7\naYg06c0hUtXQwOGaGr6uqXH+3HfqFAeqqhjVv78RGE2hMap/f/roHoZSqomGSJOeHiJldXUcPn2a\nr88Ji69raiivr2fEpZcS3q8f4f36EdavH+MHDuSKgQPp3wXOUFdKdV0aIk08PDz4oqqKusZG6kWc\nt7pvud8gQh+gj4fHN/7se4G2AX364OvpiW/fvvh6ejKwb1/6uvAXfnVDA8UOB0XfcCt0ODhcU4ND\nxBkSzUHRfH+4t7fuXSilXKIh0sTDw4NRWVl4enjg6eGBV9PPi97v04c+gGCMUmo852fDN7WLUNXY\nSEV9PRUNDVTU11PV0ED/vn2doXKhnwLOcGgOjloRgry9CfTyIsjb+4K3sH79GOrlpQe6lVLtTkOk\nibu7sxpFONXQ4AyVC/0EzgsIv759NRyUUm6jIdLE3SGilFLdUadNBa+UUkqBhohSSqk20BBRSinl\nMg0RpZRSLtMQUUop5TINEaWUUi7TEFFKKeUyDRGllFIu0xBRSinlMg0RpZRSLtMQUUop5TINEaWU\nUi7TEFFKKeUyDRGllFIu0xBRSinlMg0RpZRSLtMQ6eEyMzPdXUKXodviDN0WZ+i2aJtuGSLp6elE\nRkZisVhYv369u8vp0vQ/yBm6Lc7QbXGGbou26XYh0tDQwG233UZ6ejoHDx7kpZde4osvvnB3WUop\n1St1uxD56KOPCA8PJzQ0FC8vLxYuXMj27dvdXZZSSvVKHtLaq7K72bZt29i1axd//etfAfjb3/5G\nVlYWTz31lPM5Hh4e7ipPKaW6tdZGgmcH1dFhvktAdLNcVEqpbqvbdWeZTCasVqvzd6vVitlsdmNF\nSinVe3W7EJk8eTI5OTkcPXoUh8PBli1bmDVrlrvLUkqpXqnbdWd5enry9NNPM2PGDBoaGli2bBmj\nR492d1lKKdUrdbs9EYBrrrmGQ4cO8fXXX3P//fe3eEzPITkjNDSU8ePHM3HiRGJiYtxdTqdaunQp\ngYGBREVFOdtKS0tJSEggIiKCxMRE7Ha7GyvsPBfaFqtWrcJsNjNx4kQmTpxIenq6GyvsPFarlWnT\npjF27FjGjRvHk08+CfTOz8Y3bYvWfja63eisi2loaGDUqFG8/vrrmEwmpkyZwksvvdRr91RGjBhB\ndnY2gwcPdncpne6dd95h4MCBLFmyhM8//xyA++67jyFDhnDfffexfv16ysrKWLdunZsr7XgX2har\nV6/Gx8eHu+++283Vda6ioiKKioqIjo7m1KlTTJo0ibS0NJ5//vle99n4pm2xdevWVn02uuWeyDfR\nc0jO14P+RmiVqVOnMmjQoBZtO3bsICkpCYCkpCTS0tLcUVqnu9C2gN752QgKCiI6OhqAgQMHMnr0\naGw2W6/8bHzTtoDWfTZ6VIjYbDZCQkKcv5vNZudG6Y08PDy4+uqrmTx5svO8mt6suLiYwMBAAAID\nAykuLnZzRe711FNPMWHCBJYtW9Yrum/OdfToUfbt20dsbGyv/2w0b4u4uDigdZ+NHhUiepJhS++9\n9x779u1j586dPPPMM7zzzjvuLqnL8PDw6NWfl+XLl5Obm8snn3xCcHAw99xzj7tL6lSnTp1i7ty5\nPPHEE/j4+LR4rLd9Nk6dOsX111/PE088wcCBA1v92ehRIaLnkLQUHBwMwNChQ/npT3/KRx995OaK\n3CswMJCioiIACgsLGTZsmJsrcp9hw4Y5vyxvvvnmXvXZqKurY+7cuSxevJg5c+YAvfez0bwtfv7z\nnzu3RWs/Gz0qRPQckjOqq6uprKwEoKqqioyMjBajc3qjWbNmkZKSAkBKSorzP01vVFhY6Lz/6quv\n9prPhoiwbNkyxowZw5133uls742fjW/aFq3+bEgP89prr0lERISEhYXJww8/7O5y3ObIkSMyYcIE\nmTBhgowdO7bXbYuFCxdKcHCweHl5idlsluTkZCkpKZHp06eLxWKRhIQEKSsrc3eZneLcbbFx40ZZ\nvHixREVFyfjx42X27NlSVFTk7jI7xTvvvCMeHh4yYcIEiY6OlujoaNm5c2ev/GxcaFu89tprrf5s\n9KghvkoppTpXj+rOUkop1bk0RJRSSrlMQ0QppZTLNESUUkq5TENEKaWUyzRElFJKuez/A8eckBi2\nznADAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "price = out['p12']-out['p26']\n",
      "price.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f95acaf14d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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FXmTmcnsvbu+f1/MW5EjNpeNIzaXjSM2l40jNZcqRmkvH4fW8BTlSc+k4UnPp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\ncPMmhJAY5P8DaYsOMl28pLoAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "volume = out['v12']-out['v26']\n",
      "volume.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x59b0490>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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S0jBlyhTExMSgsrISRIS9e/diwYIFiqZvXyUlJcjKygIAZGdno7S0FB6PB21tbSgrK0NO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\n0LQpAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close = df['Adj Close']\n",
      "close = close[-26:]\n",
      "close.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f95ad56ded0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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9fbFw4UL4+voaH9fpdFi8eDFCQ0MLLpB7/CVGBOzZA3zwAVCz5uOrZzHG1EHW\nefwO/67lm5WVhezsbNTIZ1eTB3XziooSM3WuXwc+/xwICAA0GrmrYowpiaQDv8FgQOvWrREfH48x\nY8bA3d3d5HGNRoOjR4/C09MTLi4uWLhwYZ5tAGDo0KFwc3MDAFSrVg1arRb+/849zOmFleV2TEwM\nJkyYYLbXk+N2w4b+mD4dCAvTITAQ+PVXf5Qvb7p97r6h3PVKffvJzHLXI+VtW/j5Lc5t/vktfHud\nToeEhAQUC1lAWloa+fj40KFDh0zuT09Pp4yMDCIiCgsLoyZNmuR5riVKfLIua/LPP0RTphDVqEE0\nYwZRenrB21pzzpLirLZHLTmJyp61qHHTYvP4Z8+ejaeeegqTJ08ucJuGDRsiMjLSpCXEPf78ZWUB\nK1aIefi9e4tZO/Xry10VY0wJZJvHn5qairS0NADA/fv3sX//fng9cUWP5ORkY3HHjx8HEeV7HIA9\nRgT8+CPQvDmwbx9w8KCYucODPmOsuCQb+G/cuIGOHTtCq9XCx8cHAQEB6NSpE4KDgxH879U9tm3b\nBg8PD2i1WkyYMAGbN2+WqpxC5e6TKdnRo8ALLwCffQasXAmEhQEtWxb/+daS0xw4q+1RS07Aitfq\n8fDwQFRUVJ77g4KCjH9/55138M4770hVgk05fBjo10/Mw3/zTT7bljFWerxWjxW4dg3w8QFWrwa6\ndZO7GsaY0vFaPVbu/n2gTx9xVSwe9Blj5sADP5TbOyQCRo0CmjYVJ2WVlVJzSoGz2h615ASsuMfP\nyu7LL4HYWOB//+Ozbxlj5sM9foU6cAAYPBg4dgx45hm5q2GMWRO+5q4VunwZeOstce1bHvQZY+bG\nPX4oq3d47x7wyivA9OnmvxSiknJKjbPaHrXkBKx4PX5WckTA0KFAmzYAn97AGJMK9/gVZO5cYPdu\ncRH0ihXlroYxZq24x28FHj4EvvhCXCHr+HEe9Blj0uJWD+TtHep0gKcncOKEmLYp5WJr3CO1TWrJ\nqpacAM/jt1m3bokLnh86BHz1lTigy3P1GWOWwD1+CzMYxJo7U6eKefqffgpUqSJ3VYwxW8I9fgWJ\njQVGjwYePRJr6Wu1clfEGFMj7vHDMr3DxYuBDh3EkspHj8oz6HOP1DapJatacgLc47cJO3YAS5YA\n0dGAq6vc1TDG1I57/BKLjRV7+nv3At7eclfDGFMDXo9fRrdvi7X0v/ySB33GmHLwwA9p+ml6PTBg\ngJim+dZvJGXQAAAdCUlEQVRbZn/5UuEeqW1SS1a15ASseK2eBw8ewMfHB1qtFu7u7vjoo4/y3W78\n+PFo0qQJPD09ER0dLVU5FvfRR2Ltnc8/l7sSxhgzJWmPPzMzEw4ODtDr9fD19cXChQvh6+trfDws\nLAzLli1DWFgY/vjjD7z33ns4duyYaYFW2OPfuBH4+GOx/ELNmnJXwxhTG1l7/A4ODgCArKwsZGdn\no0aNGiaPh4aGIjAwEADg4+ODtLQ0JCcnS1mS5CIjxfVxf/qJB33GmDJJOp3TYDCgdevWiI+Px5gx\nY+Du7m7yeFJSEho0aGC87erqisTERNSpU8dku6FDh8LNzQ0AUK1aNWi1Wvj/u1h9Ti+sLLdjYmIw\nYcKEMr9eSgrQo4cO48YBHh7mq89ct3P3DZVQj5S3n8wsdz1S3jbXz6/Sb/PPb+Hb63Q6JCQkoFjI\nAtLS0sjHx4cOHTpkcn+vXr3oyJEjxtudOnWiyMhIk20sUeKTdZVGVhZR+/ZE06eXvR6pmCOnteCs\ntkctOYnKnrWocdNi8/hnz56Np556CpMnTzbeN3r0aPj7+2PAgAEAgGbNmiE8PNxkj1/pPX4iIClJ\nrLlz8yawaxdQjudKMcZkJFuPPzU1FWlpaQCA+/fvY//+/fDy8jLZpnfv3ggJCQEAHDt2DNWqVcvT\n5lGa1FRxMtbs2UDv3mIZZS8vMWd/wwYe9BljyifZMHXjxg107NgRWq0WPj4+CAgIQKdOnRAcHIzg\n4GAAQI8ePdCoUSM0btwYQUFBWLFihVTlFCp3nyw3gwE4fVosm/zGG0CjRuLP558Dd++K1TV//x1I\nSQG2bwecnCxbd0kVlNMWcVbbo5acgBWv1ePh4YGoqKg89wcFBZncXrZsmVQllBgRcPasWCP/0CFx\nCcTq1cVFz3v2FO2c557jvXrGmHVT9Vo9RMD582KQ1+nEH0dHMdB36AD4+QG5Jh0xxphVKGrcVNXA\nTwTExT3eo9fpgEqVxCDv7y/+PPOMWd6KMcZko+pF2oiAS5eA774T6+C7ugKdOolr23btKvrzCQlA\nYKAOgYG2P+hzj9Q2qSWrWnICVtzjlwMRcOWK2JPP2aM3GMQefYcOwKxZ4uAsX9uWMaZmVt/quXMH\n2Lnz8WCflfW4ddOhA9C4MQ/0jDF1sfkef0CAGOz79hWDfdOmPNAzxtTNpnv8e/eKWTmhoeIi5s2a\nlW7QV0vvUC05Ac5qi9SSE7Di9fillpUlVsFcvBioWFHuahhjzHpYbatn8WJg3z5gzx5u7TDGWG42\n2eNPSQHc3YEjR0R7hzHG2GM22eOfNg0YMsR8g75aeodqyQlwVluklpwAz+PPIzIS2L1bHNRljDFW\nclbV6iEC2rcHAgOBt9+WuTDGGFMom2r1bN4MZGYCw4fLXQljjFkvqxn4MzKADz4Qa+Pb2Zn3tdXS\nO1RLToCz2iK15AR4Hr/RZ5+JNo+vr9yVMMaYdbOKHv/ly4TnnwdiYnh9fMYYK4pN9PinTBFn6fKg\nzxhjZWcVA39kJDB5snSvr5beoVpyApzVFqklJ8A9fgDAwoXAU09J9/oxMTHSvbiCqCUnwFltkVpy\nAtJnlWzgv3btGjp06IAWLVqgZcuW+Oqrr/Jso9Pp4OTkBC8vL3h5eWHOnDn5vtarr0pVpZCWlibt\nGyiEWnICnNUWqSUnIH1Wyc7ctbe3x5dffgmtVot79+7B29sbnTt3RvPmzU228/PzQ2hoaKGvxYuw\nMcaY+Ui2x1+3bl1otVoAQJUqVdC8eXNcv349z3ZKmFSUkJAgdwkWoZacAGe1RWrJCVggK1nAlStX\n6Omnn6a7d++a3K/T6ahGjRrUqlUr6t69O505cybPcwHwH/7Df/gP/ynhn8JIvkjbvXv38Nprr2Hp\n0qWoUqWKyWOtW7fGtWvX4ODggD179qBPnz64ePGiyTakgG8EjDFmSyQ9gevRo0fo1asXunfvjgkT\nJhS5fcOGDREZGYkaNWpIVRJjjKmeZD1+IsKIESPg7u5e4KCfnJxs3KM/fvw4iIgHfcYYk5hkrZ7/\n/e9/2LBhA1q1agUvLy8AwLx583D16lUAQFBQELZt24ZvvvkG5cuXh4ODAzZv3ixVOYwxxv6l+LV6\nGGOMmZfdzJkzZ8pdhKWsXr0ap06dgkajQd26dWEwGKCxwZME1JITUEfWjRs3wtnZGY6OjnKXIjm1\nZJU7p1Us2VBWUVFR8PX1xZYtW5CUlIShQ4fi5s2bKFfOtuKrJSegnqyxsbEYPHgwtmzZgszMTLnL\nkZRasiohp9Vdc7eksrKycOLECYwfPx79+/cHgHxPJLN2askJqCvrnTt30Lx5c0RERMDPzw9ardbm\nvtHkUEtWJeS02VbPo0ePYGdnBzs7O3h4eMDT0xMA8O6772Lbtm1wcnKCvb096tevb9XtAbXkBNST\nVa/XQ6PRQKPR4OLFi/Dz88O9e/cQFRWF7t27y12eWaklq9Jy2tb3YgA//vgjKlWqhGHDhgEADAYD\nKlasCADYu3cvqlatiu3btyMzMxNvvvkmAFhle0AtOQF1ZF21ahXatGmDrKwslC9fHgaDAQCQlJSE\nI0eO4IsvvkBsbCwmTpyITZs2GR+3RmrJquicZlmTQSESExNpzJgxFBISQrVq1aKTJ08SEdHDhw+J\niCgrK8tk++eff55+++03i9dZVmrJSaSOrBs2bKAuXbpQs2bNaOTIkURE9OjRIyIi2rt3L+3evZuu\nXr1Kzz77LFWuXJn2798vZ7llopasSs9p9QP/7du3KTU11Xg7NjaWiIjmzp1L3t7eBT4vLi6OBg0a\nRLdv35a8RnNQS04idWTNysoig8FARETnz5+na9euUWZmJjk6OtLZs2eN2wUHB5NGoyF3d3f69ttv\nqXv37rRx40bjIGIN1JLVmnJa9cC/aNEicnZ2ptdff50mT56c5/GGDRtSSEgIEYnftnq9nq5cuUKf\nffYZabVamj17tqVLLhW15CRSR9YPP/yQunfvTh9++CFlZ2cTEZFerycioqlTp5Kvr69x26ysLFq+\nfDndv3+fiIh27NhBq1evtnzRpaSWrNaW02oH/ri4OOrSpQv9/ffflJKSQi+99BKtXbuW0tLSjNvs\n3LmT6tWrZ7xtMBgoNDSUxowZQ/Hx8XKUXWJqyUmkjqwrV66k3r170+XLl+nVV1+lcePG0ZUrV0y2\nqV+/Pm3dujXPc3MGEmuhlqzWmNOqZvWkpaWhUqVKAMRR8tWrV6Nv376oV68eqlevjt9++w3Ozs5w\nc3NDdnY23N3dcfDgQfz888/45ZdfYG9vj169eqFnz56oXr26zGkKppacgLqyAsDWrVtRv359vPrq\nq+jYsSN+/vlnZGVloUmTJsYD1o0aNcK0adPQuXNnhISEwN3dHZUqVTI5YE1Eip+1pJas1pjTKqY+\n3Lt3DxMnTkS/fv2wZMkSnD59GpUqVUK7du1w9uxZAECfPn1QtWpVnDx5EllZWbCzswMA2NnZISws\nDC1btkTPnj3ljFEkteQE1JH13r17+Pjjj7FkyRJERUUBAFq0aIGKFSsiNTUVzs7OCAgIwMmTJ00u\nvNG3b19cvHgR7dq1Q9WqVeHk5JTntZU2EKolq63kVPzAHx0djS5duqBChQqYOXMmUlJS8M033xj/\n8aKioowLvwUEBGDTpk2oUKECAGDFihVo3Lgxbty4gUmTJskZo0hqyQmoI+u2bdvg7e2N9PR03Lx5\nE3PmzMGZM2fwzDPPICkpCefOnQMA9OvXD3fu3MGZM2cAAJcvX8arr76KESNGICkpCSNHjpQzRrGo\nJast5VR8q+f+/fuoU6cOJk2ahGeeeQZ6vR6xsbHo3bs3KleujPDwcDx8+BBeXl5wc3PD9u3b8cIL\nL6BGjRrQarXo0aMH7O3t5Y5RJLXkBNSR9ZdffsHIkSMxduxYeHt74/z586hQoQK6du2K8PBw/PPP\nP3B2dkbNmjVx69YtHD58GH369EH16tXRsWNHDBgwAPb29iYn/iiVWrLaUk5FLtmQu9fVqFEj1K5d\n23ifg4MD4uPjAQA+Pj5ISUlBSEgITpw4gatXr6Jy5cpwdXUFAMUPDrmpJSdgu1lJTJZAuXLlMGzY\nMDg4OMBgMKB69eq4dOkSWrVqhXLlyqF///746aef8MEHH2DOnDnYuXMnhg8fbnwdZ2dn48k85csr\n8n9RAOJEOjVktcWcimn16PV6LFy4ELdu3crzm7BKlSrG++Lj49GiRQvjYwEBAVi6dCmaNm2KLl26\nYOfOncaDhUqk1+tx5coVPHz4EIDppSVtKScgsl66dMmms+r1epw4ccJ4O+dgXa1ateDg4ABA5H7q\nqadQr149AEDbtm0xdepUtGrVCtOmTUPbtm0xcOBAk9ctV66c4s4+JiLo9XoAjwdDwPayEhGysrIA\n2HBOy08kyis4OJhefPFFGjFiBGVkZBjnweaWc9/HH39Ma9asISIxtc8apvDlWLVqFTVt2pT69u1L\nvXv3Np7skZst5CQi2rRpE7m5udHAgQNp2LBh+Z5UZe1Z161bR61bt6ZPPvkk388yR2pqKnl7exvP\nNs45mSc7O9t4X85tpQoODqYOHTpQUFAQXbhwocDtrD3r8uXLyc/Pj9577z2TKZlPfr7WnlP2Xz97\n9uzB6NGjsWrVKqxatQoODg7G34qUaw8x577Tp0/j6tWr6N27NzZs2GA86KdkBoMBmzZtwvr167Fl\nyxbs2LEDKSkpWL9+PQAgOzvbuK0158xx48YNrFu3Dj///DN++OEHODg44LPPPsOVK1cAPP5crTUr\nEaFDhw5YsGABtm7dipkzZxbar42Pj8dzzz2HuLg4dO7cGd9//71xj7JChQowGAzGFpHS3Lt3D2+9\n9Ra2bduGJUuWoHLlypg8ebKx5idZc9YNGzZg165dWL9+Pdzc3DB//nwcOHAAQN4ZN9acE4A8e/xP\nnprcqlUr2rVrF2VnZ9P8+fNp9+7dxrPacktOTqZatWpRu3btaNOmTZYqt9Ryn3h08eJF+uuvv4y3\n165dS3369Mn3edaWk4jozp07xr8nJSVRt27d6NSpU0RE9Ntvv5GbmxstW7Yszwkr1pQ1997b3Llz\nqV27dkQk8p44ccJkmYnctmzZQhqNhv7zn//Qxo0bLVKrudy5c4d27txJGRkZRCT2fJs2bUqRkZH5\nbm/NWRcvXkwTJ0403u7atSsFBgZSXFwcEZnu9VtzTiIZTuD64IMPcPLkSbRs2RJPPfUUADEPtlu3\nbti1axcaNmyIH374AWfOnEHjxo1NLr5euXJl1KxZE8uXL0fLli0tWXaJzZo1C9OmTcOlS5dw9+5d\n+Pr6okqVKsY9gB07dqBp06Z44YUX8py4YU05AZF16tSpiI+Px71796DVahEbG4vjx4+jffv2+OWX\nX0BEsLe3R/PmzVG1alXjc60l69SpU7F9+3YQEZo2bYr27dtj/vz52L59O9avX4/4+HgsWbIE3t7e\nqF+/PoDHkxSio6Ph7u6ODRs2wMPDAwAUvWz0mjVrULFiRTg7O6NixYqoW7cuHB0d8fDhQxgMBhw8\neBADBw40OU6Tw5qyrlmzBpUqVYKzszMA4OLFi0hNTUXt2rVRt25d6HQ64+SDli1bQqPRWO1nmoel\nfsNkZmbSjBkzqEGDBvT6668bV1DM+S365Zdf0p9//klERFevXqUePXpQRESE8flK7ZU96caNG9S/\nf38aPHgwnT59mjZs2EDt2rUz7g3m9P+CgoJo586deZ5vTaeqF5Q1PT2dLly4QO+++y5169aNhg0b\nRqdOnaIXX3yRkpOTjc+3hs/00aNHNGTIEBowYAD9+OOP9PLLL9PHH39MRES//vorde7cma5fv05E\nRNOmTaMpU6YYv9Hm1/dX8oJj8fHx5O3tTRUqVKClS5fme1wmIyODfHx8KCUlxeT+/D5LpWZ9MmfO\n/5sXLlygTz/9lDp27Eht27alSZMm0bRp04zrP+n1eqv7TAtisYH/0aNHdOrUKbp37x7NnDmTZsyY\nYdL6eFL//v1p165dlirPbNLT002++iUnJ9PQoUMpJibGZLuOHTtScnIyRUdH06xZs0zaQtYiv6xD\nhgyh06dPG++7efOm8e+vv/46nT17ttADoUpiMBjIYDDQqFGj6I8//iAionPnzlGDBg2MB/NyLwt9\n+vRpateuXb5tSiLl/6KLj4+n/fv3065du2jUqFEUHh6eZ5vNmzdTYGAgERFFRkYad9aepOSsT+bU\n6XQmj8fExBhbWeHh4QW2ZImUnbMwFjvyUL58eTz33HOoXLky+vfvj8uXL+PkyZN49OhRzjcPAMDN\nmzfx7rvv4q+//oJWq7VUeWZBRHB0dERAQIDxPo1Gg5iYGLi4uBjvu3DhAtLS0jBp0iQMHz4c9evX\nz/cUbiUrKOvp06eNU9yICHXq1EF8fDxGjhyJ9PR0NGrUyGq+Dms0GqSlpSEjIwN6vR7Z2dlo1qwZ\n3njjDcyaNQsAjMtIpKam4uuvv4avr6/xvicp9kDfvxo0aAA/Pz/07t0bjo6OCA8PR1JSEoDHExBu\n3ryJevXqYeLEiejfvz/+/vvvfF9LyVmfzBkREWHMCQCenp5o3bo1UlJSEBwcjFdeeaXA11JyzsJI\nUnXuWSrA40E9Zy528+bN8fzzz0On0+HixYvGbS5cuID+/fvDwcEBhw8fxtNPPy1FeWbzZM4cjo6O\nxr///fffcHFxQa1atYz3ZWZm4tKlS2jYsCGOHz+OESNGSF5rWZUmq0ajQXZ2NoKCguDo6Iiff/7Z\nuGiVEtETs1SICNWrV4erqyvWrFljHNDnz5+PQ4cO4dSpUyhXrhw2bdqEtm3bokaNGpg3b56iTzLL\n8WRWQJwcl3Ny0ZtvvokLFy7g+PHj0Ov1xuzh4eH4/PPP4ezsjHPnzsHPz8+idZdUSXPmPCcsLAwe\nHh5wdXXFW2+9ZdGaLcLcXyFyf/U5ffp0np51zuMpKSk0ZswY2rJlC33//fe0b98+IiL6+++/zV2S\nJIrKmdPOCAsLM16BZ9euXfTHH3/Q/fv3TVogSlearKGhocav0Pfu3bNQpaVjMBjyfGXPzs425rxz\n5w55eHjQgQMHjG2ciRMnGmch/fnnnyZzvpV8nKagrPmZP38+TZ8+nVJTU42trrCwMJOsSu1vlzbn\nsWPHiEgci0tISCjyudZKkh7/hQsXqEePHjRw4EC6fPlynsdzBoo5c+aQk5MTNWnShA4ePChFKZIq\nKicR0UcffUSDBw+m4cOHk6+vr/F/IGtT0qzt27e3iqy5B+mzZ8/Sd999Z9Kjz+nhr1q1it544w1a\nu3YtnTlzhnx8fCg6OjrPayl5gCgqa47cGbp27UqNGjWiJk2a0K1bt0xeS6lZy5KzadOmdOPGDeNr\nKDlnWZR54H9y7+aff/6hQYMG0YoVKwp8jsFgoOjoaGrQoAEtWrSorCVYRElz5hwYDAgIoEaNGhX6\n76E0aspKRHT//n36/vvvqU2bNvTSSy/RuHHjjHt+uf8tQkNDadSoUeTl5UULFiyQq9wyKSxr7oPu\ner2evvzyS3rqqafo66+/lqvcUlNLztIq9cCf3ynMRKKF85///Mc4Yyf36cu5ZWRk0IMHD0r79hZT\n1py5T35ROjVkffKXml6vpxEjRpCHhwcRiZ/LGTNm0CeffGI8KS13O+P+/fsme49KnqFUmqw5efR6\nPf3+++8mJ+Ypta2jlpzmVKoTuHIvXHTgwAEMHDgQsbGxSEtLQ82aNXHt2jU0aNAATz/9tPGgUM6V\nlnJOcsh9gEWpypJTr9ejXLlyaNasmVUc7FNL1pyMcXFxsLOzg4ODAypVqoSVK1di8ODBqFmzJh4+\nfIgzZ85Ar9fD3d3dZOZGuXLlYG9vj+zsbNmX1i1KabLm5ClXrhxcXV1RsWJF4zLCBc1WkptacppT\nsQf+69ev4/Lly6hatSrKly8PjUaDI0eO4KuvvsJ3332HGjVqYOzYsejZsycSEhJw7tw5VKhQAVWr\nVsX48eORnp6O1q1bK/p/FMB8Oa1hmpdasr7//vs4fvw4/Pz8cPHiRYwZMwabN2/GL7/8gsaNG+Ol\nl15CQkICIiIiEBAQgHr16uH8+fM4ceIEWrdubXKmce4BQ4k/y+bMmkOJWdWSUzJFfSXQ6/U0depU\nat68OfXu3Zu6du1Ks2bNIiKigwcP0uzZs2nBggX0/PPP0/z584mI6Nq1a7RixQrq0aMHeXh40Lx5\n86T93mIGaslJpK6sREQRERFUvXp1Sk9PpzFjxtDKlSuJiMjPz498fX3pwYMHxrM5jx49SkREf/zx\nh1VOOFBLVrXklEqhA/+ePXuodu3aNHXqVLp16xZlZmbSkSNHyNHRkQ4ePEihoaHk7u5OI0eONPaD\nU1NT6erVq0QkFq9KT0+XPkUZqSUnkbqyEj3u5fbt25eCgoKIiOjEiRPUrl07mjBhAnl7e9MXX3xB\nRESffPIJtW/fXrZay0otWdWSU0qFDvz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      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}